Logistics Digital Twin : A key step in your Digital Twin journey

Manufacturing Digital twins are becoming the new normal fast

Chances are that every time you hear about Digital Twins, it is in the context of Manufacturing. Rapid developments in the domain of Big Data and sensor technologies, in tandem with advances in the field of IIoT technologies have led to advancement of Digital Twins, but the focus has primarily been in manufacturing. If you follow the Smart Manufacturing topic, you know that there are a multitude of “Lighthouses” – model factories that already have robust Manufacturing Digital Twins. Ecosystems and platforms are being developed at a rapid pace and significant research has been done in this area.

Logistics Twin is the hidden Gem

Like Manufacturing Digital twins, if Logistics assets are equipped with a comprehensive sensor system, a Digital image of the respective logistics object can be created, where real time data exchange happens between the actual physical object and the Digital Image. This is the vision that I have in my perspective of a Logistics Digital Twin.

You will rarely find content on Logistics Digital Twins. One key reason behind that in my mind is that there is a lack of concrete use cases in Logistics domain. In terms of implementation, Logistics Twin will be more difficult to implement, considering the nature of the function. Unlike manufacturing, Logistics assets travel far and wide, movement is the crux of this function and more comprehensive architecture is required.

Is the concept different from Manufacturing twins ?

We know that Logistics functions of large organizations generate a vast amount of data, which is mainly generated by controlling and monitoring large quantities of flow of goods. This data holds considerable potential in terms of visibility, optimization, risk management and control of Logistics function.

Note that the value of the data generated is not measured by the amount of data collected but by the applications made possible by the data. Making use of this data requires a substantial and valid data basis. Remember that Data collection is no longer a challenge in this age of cheap sensor technology and open source applications. The key is to understand how this data is leveraged.

The technical architecture required is not significantly different or complex vs Manufacturing Digital twin.  ( My this post discusses my take on what the Technical architecture of a Logistics Digital Twin looks like : Link )

What capabilities can it provide ?

As mentioned above, data collection and processing is the not the key aspect of a Digital Logistics twin – what you do with the data is what provides you unique capabilities.

Unique capabilities are capabilities that your competitors does not have, that need significant development and time to build, which will make sure that your competitors will always be years ahead if they want to catch-up. These are the types of capabilities that you call Game Changing.

The capabilities in case of Digital Logistics Twin can be:

(1) Real time monitoring

The definition, calculation and visualization of KPIs, in my perspective, is the central analysis function of a Digital twin system. KPIs generated by Digital Twins can enable companies to quickly determine the condition of their assets and the efficiencies of their processes. Three steps are required to define and store a new KPI function for a specific scenario:

  • Implementation of the KPI function
  • Implementation of KPI visualization
  • Adding a semantic description to a KPI record/event

(2) Real time planning

I am a not a big fan of plain monitoring if you can’t take real time corrective action to course correct.This is where Machine learning models can be really useful. Imagine a scenario where an algorithm detects that a shipment has got stuck due to a natural disaster. It can then locate the next shipment, which can be expedited in the most cost efficient way, to make sure that there are no disruptions due to that lost shipment.

(3) Continuous improvement

Performing analysis on historical data from Digital Twin combined with insights from real time scenarios will help you identify bottlenecks in your processes. The continuous improvement process becomes significantly easier, not only in identifying the opportunities but also in terms of observing impact of improvements made.

(4) End to end Supply Chain integration

Combining Digital twin in Logistics with Manufacturing and warehouse Digital Twins, encompassed with a control tower will provide you an end to end visibility into your Supply Chain. Imagine the end to end optimization opportunities once you have this visibility.

(5) New products and services

This is the most important and truly fits in the bucket of “Game changing” aspect that I mention frequently in my posts. Since every company in every industry can create a different product or service, I can’t quote a specific example here but you need to believe my word when I say that this is what will provide the true “competitive edge” that I keep on talking about.


Views my own.



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